Rice extraction method and device based on multi-temporal SAR (Synthetic Aperture Radar) data

Abstract

The invention discloses a rice extraction method and device based on multi-temporal SAR (Synthetic Aperture Radar) data, relates to the technical field of agricultural production monitoring, and mainly aims to improve rice identification and extraction precision. According to the main technical scheme, the method comprises the steps of obtaining multi-temporal SAR data corresponding to a target year of a to-be-extracted region and a backscattering coefficient corresponding to the multi-temporal SAR data; synthesizing the multi-temporal SAR data into a multi-band SAR image according to respective preset backscattering coefficient combination modes of different time phases; determining a backscattering coefficient combination classification threshold value corresponding to each wave band according to the backscattering coefficient combination value corresponding to each wave band in the second multi-wave band SAR image in the historical year and the rice distribution proportion; and introducing the back scattering coefficient combination classification threshold value corresponding to each wave band into a preset extraction rule, and performing rice extraction on the first multiband SAR image of the current year by using the extraction rule to obtain a rice distribution diagram corresponding to the to-be-extracted region in the current year.

Description

Technical Field

[0001] The present application relates to the technical field of agricultural production monitoring, and in particular to a rice extraction method and device based on multi-temporal SAR data. Background Art

[0002] With global climate change and population growth, food security issues are receiving increasing attention. As one of the world's major food crops, rice's planting area and yield have an important impact on global food supply. In order to improve the efficiency and sustainability of agricultural production, it is essential to accurately monitor rice planting distribution, growth status, and predict yield. With the continuous development of remote sensing technology, Synthetic Aperture Radar (SAR) data has become an effective means of monitoring and extracting rice and other crops because of its advantages such as being unaffected by weather conditions and being able to be obtained all day and all weather.

[0003] At present, in the existing technology, when extracting rice from Synthetic Aperture Radar (SAR) data, the polarization value in the backscatter coefficient is usually used alone. However, rice has different physical structures and growth states at different growth stages. Relying on a single polarization value cannot fully reflect the complex changes of rice at various growth stages, resulting in low accuracy in rice recognition and extraction. Summary of the invention

[0004] In view of the above problems, the present application provides a rice extraction method and device based on multi-temporal SAR data, the main purpose of which is to improve the accuracy of rice recognition and extraction.

[0005] In order to solve the above technical problems, this application proposes the following solutions:

[0006] In a first aspect, the present application provides a rice extraction method based on multi-temporal SAR data, the method comprising:

[0007] Acquire multi-phase SAR data corresponding to the target year of the area to be extracted and backscatter coefficients corresponding to the multi-phase SAR data, wherein the target year includes the current year and historical years;

[0008] The multi-phase SAR data are synthesized into a multi-band SAR image according to a combination method of backscatter coefficients preset for different phases, wherein the multi-band SAR image includes a first multi-band SAR image of the current year and a second multi-band SAR image of the historical year;

[0009] Determine the backscatter coefficient combination classification threshold corresponding to each band according to the backscatter coefficient combination value corresponding to each band in the second multi-band SAR image of the historical year and the proportion of rice distribution;

[0010] The backscatter coefficient combination classification threshold corresponding to each band is introduced into a preset extraction rule, and the extraction rule is used to extract rice from the first multi-band SAR image of the current year to obtain a rice distribution map corresponding to the area to be extracted in the current year.

[0011] In a second aspect, the present application provides a rice extraction device based on multi-temporal SAR data, the device comprising:

[0012] An acquisition unit, used to acquire multi-phase SAR data corresponding to a target year of an area to be extracted and a backscatter coefficient corresponding to the multi-phase SAR data, wherein the target year includes a current year and a historical year;

[0013] a processing unit, configured to synthesize the multi-phase SAR data obtained by the acquisition unit into a multi-band SAR image according to a combination of backscatter coefficients preset for different phases, wherein the multi-band SAR image includes a first multi-band SAR image of the current year and a second multi-band SAR image of the historical year;

[0014] A first determining unit is used to determine a backscatter coefficient combination classification threshold corresponding to each band according to the backscatter coefficient combination value corresponding to each band in the second multi-band SAR image of the historical year obtained by the processing unit and the proportion of rice distribution;

[0015] The extraction unit is used to introduce the backscatter coefficient combination classification threshold corresponding to each band obtained by the first determination unit into a preset extraction rule, and use the extraction rule to extract rice from the first multi-band SAR image of the current year obtained by the processing unit to obtain a rice distribution map corresponding to the area to be extracted in the current year.

[0016] In order to achieve the above-mentioned purpose, according to the third aspect of the present application, a storage medium is provided, which includes a stored program, wherein when the program is running, the device where the storage medium is located is controlled to execute the rice extraction method based on multi-temporal SAR data of the above-mentioned first aspect.

[0017] In order to achieve the above objective, according to a fourth aspect of the present application, a processor is provided, the processor being used to run a program, wherein when the program is run, the rice extraction method based on multi-temporal SAR data of the above first aspect is executed.

[0018] By means of the above technical scheme, the present application provides a rice extraction method and device based on multi-temporal SAR data. When it is necessary to extract the distribution of rice, firstly obtain the multi-temporal SAR data corresponding to the current year and the historical year of the area to be extracted, and then synthesize the multi-temporal SAR data into a multi-band SAR image according to the preset backscatter coefficient combination mode of different phases, so as to obtain the first multi-band SAR image of the current year and the second multi-band SAR image of the historical year, and then determine the backscatter coefficient combination classification threshold corresponding to each band according to the backscatter coefficient combination value corresponding to each band in the second multi-band SAR image of the historical year and the proportion of rice distribution, and finally introduce the backscatter coefficient combination classification threshold corresponding to each band into the preset extraction rule, and use the extraction rule to extract rice from the first multi-band SAR image of the current year, so as to obtain the rice distribution map corresponding to the area to be extracted in the current year. The technical solution provided by the present application introduces multi-phase SAR data of the current year and historical years, and synthesizes the multi-phase SAR data according to the combination of backscatter coefficients of different phases, so that the obtained multi-band SAR image can more comprehensively reflect the scattering characteristics of rice in different years and different growth stages, so that it can capture more details and features, which is helpful to more accurately identify and extract rice, and use the multi-band SAR images of historical years to determine the rice extraction threshold corresponding to the multi-band SAR images of the current year, fully considering past experience and rules, providing a reliable reference standard for rice extraction in the current year, reducing uncertainty and error, and accurately reflecting the rice distribution in the area to be extracted in the current year, thereby effectively improving the accuracy of rice identification and extraction.

[0019] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0021] Figure 1 A flow chart of a rice extraction method based on multi-temporal SAR data provided in an embodiment of the present application is shown;

[0022] Figure 2 Another flow chart of a rice extraction method based on multi-temporal SAR data provided in an embodiment of the present application is shown;

[0023] Figure 3 A block diagram of a rice extraction device based on multi-temporal SAR data provided by an embodiment of the present application is shown;

[0024] Figure 4 A block diagram of another rice extraction device based on multi-temporal SAR data provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0025] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0026] At present, in the prior art, when extracting rice through Synthetic Aperture Radar (SAR) data, the polarization value in the backscatter coefficient is usually used alone to achieve it. For example, the horizontal polarization value (VV) or the vertical polarization value (VH). However, rice has different physical structures and growth states in different growth stages. Relying on a single polarization value cannot fully reflect the complex changes of rice in various growth stages, and fails to fully combine the combined characteristics of the backscatter coefficient of the rice growth cycle, resulting in low accuracy in rice recognition and extraction.

[0027] After research, the inventor found that it is possible to integrate multi-phase SAR data of historical and current years, and synthesize the multi-phase SAR data into multi-band SAR images according to the preset backscatter coefficient combination mode of different phases to cover the change characteristics of the entire growth cycle of rice, and automatically determine the classification threshold based on the backscatter coefficient combination value of each band in the multi-band SAR images of historical years and the rice distribution ratio, introduce the determined classification threshold into the preset extraction rule, process the multi-band SAR images of the current year, and generate a high-precision rice distribution map. In this way, not only the polarization value (such as VV or VH) of a single phase is considered, but also the backscatter coefficient changes of multiple phases are combined, and the backscatter coefficient combination characteristics of rice at different growth stages can be fully utilized, effectively improving the accuracy of rice identification and extraction, and realizing more accurate and reliable rice distribution map drawing.

[0028] To this end, the present application embodiment provides a rice extraction method based on multi-phase SAR data, through which the rice recognition and extraction accuracy can be improved. The specific execution steps are as follows: Figure 1 As shown, including:

[0029] 101. Obtain the multi-temporal SAR data corresponding to the target year and the backscatter coefficient corresponding to the multi-temporal SAR data of the area to be extracted.

[0030] Among them, the target years include the current year and historical years.

[0031] It should be noted that, in this embodiment, the area to be extracted may be one or more geographical areas where rice distribution needs to be monitored. The current year refers to the year in which rice is to be extracted in the area to be extracted, and the historical year may be the year in which the area to be extracted has complete multi-temporal historical SAR data and is the most recent year to the current year.

[0032] In this step, SAR data covering the area to be extracted can be obtained from the satellite database. Common SAR data sources include Sentinel-1 of the European Space Agency and RADARSAT series of the Canadian Space Agency. Ensure that the selected data has sufficient temporal and spatial resolution to meet the needs of rice identification, such as a spatial resolution of about 10 meters and a time interval ranging from one week to one month. Extract the backscatter coefficient of each phase SAR image, usually including values ​​under different polarization modes such as horizontal polarization (HH) and vertical polarization (VV). Arrange the SAR data in time series to form a multi-phase SAR data set covering the entire growth cycle, including data from multiple key growth periods such as transplanting period, jointing period, and filling period.

[0033] Specifically, the multi-phase corresponding to rice can be determined by analyzing the phenological characteristics of the area to be extracted, and the multi-phase includes the transplanting period, the jointing period and the filling period. The first multi-phase SAR data of the current year and the second multi-phase SAR data of the historical year are obtained in a targeted manner according to the multi-phase, and the first multi-phase SAR data and the second multi-phase SAR data are preprocessed to extract the backscattering coefficients corresponding to the first multi-phase SAR data and the second multi-phase SAR data. The preprocessing includes calibration, multi-viewing, filtering, polarization decomposition, geocoding, etc.

[0034] 102. According to the preset backscatter coefficient combination method of different phases, the multi-phase SAR data are synthesized into a multi-band SAR image.

[0035] The multi-band SAR images include the first multi-band SAR images of the current year and the second multi-band SAR images of the historical years.

[0036] In this step, the backscatter coefficient combination mode of each phase can be preset according to the physical structure change characteristics of rice at different growth stages. The backscatter coefficient combination mode includes: sum value, ratio and radar vegetation index (Dprvivv). Among them, the sum value is VV+VH, the ratio is VH / VV, and Dprvivv is 4*VH / (VH+VV). Specifically, a comparison relationship table of different phases and different backscatter coefficient combinations can be constructed and maintained to facilitate subsequent table lookup and determination.

[0037] According to the backscattering coefficient combination method, different polarization values ​​in the multi-phase SAR data are combined to generate two groups of multi-band SAR images, one group is the first multi-band SAR image of the current year, and the other group is the second multi-band SAR image of the historical year. It should be noted that each band represents the backscattering characteristics of rice in a specific phase. For example, the sum of the backscattering coefficients of the first phase of the transplanting period (VV+VH), the sum of the backscattering coefficients of the second phase of the transplanting period (VV+VH), the ratio of the backscattering coefficients of the first phase of the filling period (VH / VV), the Dprvivv (4*VH / (VH+VV)) of the backscattering coefficients of the jointing period, and the Dprvivv (4*VH / (VH+VV)) of the backscattering coefficients of the second phase of the filling period. By using the combined features, the obtained multi-band SAR images can more comprehensively reflect the scattering characteristics of rice in different years and different growth stages, so that more details and features can be captured, which is helpful for more accurate identification and extraction of rice in the subsequent period.

[0038] 103. According to the combined backscatter coefficient values ​​corresponding to each band in the second multi-band SAR image of the historical year and the proportion of rice distribution, the combined backscatter coefficient classification threshold corresponding to each band is determined.

[0039] In this step, the backscatter coefficient combination values ​​of each band in the second multi-band SAR image of the historical year are analyzed, and the statistical parameters such as the mean and standard deviation are calculated. The proportion of rice distribution is determined by using the historical rice distribution information corresponding to the known historical years (rice distribution map or ground measured data, etc.). The backscatter coefficient combination classification threshold corresponding to each band can be determined by the backscatter coefficient combination values ​​corresponding to each band and the proportion of rice distribution. Specifically, a histogram in which the horizontal axis corresponding to each band is the backscatter coefficient combination value and the vertical axis is the proportion of the distribution quantity can be drawn, and the proportion of rice distribution corresponding to each band can be referred to. The troughs with similar proportions are selected from the troughs on both sides of each peak in the histogram as classification troughs, and the backscatter coefficient combination values ​​corresponding to these classification troughs are used as classification thresholds for each band. It is also possible to use known ground measured data, historical high-precision remote sensing data and other rice distribution data as training sets to train a random forest, support vector machine, neural network, etc. as a classifier, and automatically learn the relationship between the backscatter coefficients of different bands and the rice distribution through these classifiers, and generate a classification threshold. This embodiment does not limit this.

[0040] In addition, in the process of determining the classification threshold by analyzing the histogram, factors such as the growth stage of rice and plant density can also be combined to make the backscatter coefficient combination classification threshold more accurate.

[0041] 104. The backscatter coefficient combination classification threshold corresponding to each band is introduced into the preset extraction rule, and the extraction rule is used to extract rice from the first multi-band SAR image of the current year to obtain the rice distribution map corresponding to the extracted area in the current year.

[0042] In this step, according to the determined classification threshold, a specific extraction rule is formulated. The extraction rule can be set for each band, that is, one band corresponds to one extraction rule, which is used to make each band more accurate when classifying and extracting rice. For example, it can be set that when the backscatter coefficient combination value of a certain pixel is greater than or less than a certain specific classification threshold, it is classified as rice. The formulated extraction rule is applied to the first multi-band SAR image of the current year, and it is judged pixel by pixel whether it belongs to the rice category. This process can use machine learning algorithms (such as random forests, support vector machines, etc.) to assist classification and further improve accuracy. The classification results of all pixels are combined to generate a final rice distribution map to show the specific distribution of rice in the area to be extracted.

[0043] It should be noted that in order to reduce background noise interference and ensure the accuracy of rice extraction, the first multi-band SAR image of the current year can also be segmented and denoised to remove background noise (such as non-agricultural land, waters, etc.), so that the subsequent classification process is more focused on rice, thereby improving the accuracy of classification and avoiding misclassification caused by noise. On this basis, since the segmented sub-images usually contain fewer types of objects and more uniform texture information, which makes the backscattering coefficient and other features more obvious, the new first multi-band SAR image obtained after the initial segmentation and denoising can be segmented twice to obtain multiple new first multi-band SAR sub-images, and the above-mentioned extraction rules are used to extract rice from multiple new first multi-band SAR sub-images in turn, which is conducive to distinguishing different types of objects, thereby improving the accuracy of rice classification and extraction.

[0044] Based on the above Figure 1 It can be seen from the implementation method that the present application provides a rice extraction method based on multi-phase SAR data. When it is necessary to extract the distribution of rice, first obtain the multi-phase SAR data corresponding to the area to be extracted in the current year and the historical year, and then synthesize the multi-phase SAR data into a multi-band SAR image according to the preset backscatter coefficient combination method of different phases, so as to obtain the first multi-band SAR image of the current year and the second multi-band SAR image of the historical year, and then determine the backscatter coefficient combination classification threshold corresponding to each band according to the backscatter coefficient combination value corresponding to each band in the second multi-band SAR image of the historical year and the proportion of rice distribution, and finally introduce the backscatter coefficient combination classification threshold corresponding to each band into the preset extraction rule, and use the extraction rule to extract rice from the first multi-band SAR image of the current year to obtain the rice distribution map corresponding to the area to be extracted in the current year. The technical solution provided by the present application introduces multi-phase SAR data of the current year and historical years, and synthesizes the multi-phase SAR data according to the combination of backscatter coefficients of different phases, so that the obtained multi-band SAR image can more comprehensively reflect the scattering characteristics of rice in different years and different growth stages, so that it can capture more details and features, which is helpful to more accurately identify and extract rice, and use the multi-band SAR images of historical years to determine the rice extraction threshold corresponding to the multi-band SAR images of the current year, fully considering past experience and rules, providing a reliable reference standard for rice extraction in the current year, reducing uncertainty and error, and accurately reflecting the rice distribution in the area to be extracted in the current year, thereby effectively improving the accuracy of rice identification and extraction.

[0045] Further, the preferred embodiment of the present application is in the above Figure 1Based on this, a detailed description of the rice extraction process based on multi-temporal SAR data is given. The specific steps are as follows: Figure 2 As shown, including:

[0046] 201. Obtain multi-temporal SAR data corresponding to the target year and backscatter coefficients corresponding to the multi-temporal SAR data of the area to be extracted.

[0047] This step is combined with the description of step 101 in the above method, and the same content is not repeated here. It should be noted that the specific execution process of obtaining the multi-phase SAR data corresponding to the target year of the area to be extracted and the backscatter coefficient corresponding to the multi-phase SAR data is: determine the multi-phase corresponding to rice according to the phenological characteristics of the ground objects in the area to be extracted, and the multi-phase includes the transplanting period, the jointing period and the filling period; obtain the SAR data corresponding to the target year of the area to be extracted according to the multi-phase, and obtain the multi-phase SAR data, and the multi-phase SAR data includes the first multi-phase SAR data of the current year and the second multi-phase SAR data of the historical year; pre-process the first multi-phase SAR data and the second multi-phase SAR data, respectively, to obtain the backscatter coefficients corresponding to the first multi-phase SAR data and the second multi-phase SAR data.

[0048] In this step, the historical meteorological data, soil type, topography and other information of the area to be extracted are collected and analyzed in advance to understand the growth cycle of the local main crops (especially rice). Specifically, the information provided by agricultural science literature or local agricultural departments can be referred to to clarify the main growth stages of rice in the area and their time distribution. At the same time, the key periods of rice growth are determined, including the transplanting period, jointing period and filling period. These periods usually correspond to specific farming activities and physiological changes, and are important time nodes for monitoring and identifying rice. For example, in southern China, the transplanting period of rice is generally from April to May each year, the jointing period is from June to July, and the filling period is from August to September.

[0049] The key growth period determined above is used as the multi-temporal phase corresponding to rice to ensure that each key period has corresponding image coverage. According to the multi-temporal phase, a suitable SAR data source is selected from the public satellite database to obtain the SAR data corresponding to the target year of the area to be extracted, including the current year and the historical year. The time point of the SAR data is as close as possible to the central period of the key growth period. All downloaded SAR images are arranged in time series to form a multi-temporal SAR data set containing multiple key periods such as transplanting period, jointing period, and filling period, and the first multi-temporal SAR data and the second multi-temporal SAR data are obtained. The first multi-temporal SAR data and the second multi-temporal SAR data are preprocessed respectively, including calibration, multi-viewing, filtering, polarization decomposition, geocoding, etc., and the backscattering coefficient of each phase SAR image is extracted as the basic feature for subsequent classification and analysis. The backscattering coefficient reflects the reflection intensity of the surface to the radar wave and is an important indicator for distinguishing different types of land objects.

[0050] 202. According to the preset backscattering coefficient combination method of different phases, the multi-phase SAR data are synthesized into a multi-band SAR image.

[0051] This step is combined with the description of step 102 in the above method, and the same contents are not repeated here.

[0052] 203. According to the combined backscatter coefficient values ​​corresponding to each band in the second multi-band SAR image of the historical year and the proportion of rice distribution, the combined backscatter coefficient classification threshold corresponding to each band is determined.

[0053] This step is combined with the description of step 103 in the above method, and the same content is not repeated here. It should be noted that, according to the backscatter coefficient combination value corresponding to each band in the second multi-band SAR image of the historical year and the proportion of rice distribution, the specific execution process of determining the backscatter coefficient combination threshold value corresponding to each band is as follows: based on the backscatter coefficient combination value corresponding to each band in the second multi-band SAR image and the proportion of the distribution quantity corresponding to the backscatter coefficient combination value, a histogram corresponding to each band is drawn, the abscissa of the histogram is the backscatter coefficient combination value, and the ordinate is the proportion of the distribution quantity; referring to the proportion of rice distribution corresponding to each band, the classification trough corresponding to each band is determined in the troughs on both sides of each peak in the histogram corresponding to each band using the principle of proximity, and the backscatter coefficient combination value corresponding to the classification trough is used as the backscatter coefficient combination threshold value corresponding to each band.

[0054] In this step, the horizontal axis is set to the backscatter coefficient combination value, the range should cover all possible value ranges, and the vertical axis is set to the proportion of the distribution number, indicating the frequency of each backscatter coefficient value. Use professional geographic information system (GIS) software or programming languages ​​(such as Python, R) in the drawing library (such as Matplotlib, Seaborn, etc.) to draw the histogram. For each band, draw its corresponding histogram separately to ensure that each histogram can clearly show the distribution of the backscatter coefficient value.

[0055] The key points of the rice distribution ratio are marked on the histogram so that they can be quickly located in the subsequent analysis. At the same time, the histogram of each band is analyzed to identify the troughs (local minimums) on both sides of each peak (local maximum), where the peaks usually represent the concentrated backscatter coefficient values, and the troughs indicate the dividing points between different categories. According to the proportion of rice distribution, the trough position closest to the rice ratio is found in the troughs on both sides of each peak, and the trough position is the classification trough. Try to choose those troughs between two obvious peaks to ensure that the classification boundaries are clear and reasonable. The backscatter coefficient combination values ​​corresponding to the classification troughs of each band are recorded, and these values ​​are the classification thresholds corresponding to each band. The classification threshold is used to distinguish rice from other landforms and is an important part of the subsequent classification rules. If there are multiple candidate troughs, the optimal classification threshold can be selected through further analysis (such as considering the stability of adjacent troughs, consistency with other bands, etc.).

[0056] It is worth noting that in this step, the combined value of the backscatter coefficients corresponding to the troughs on both sides of the peak is selected as the classification threshold, which can establish a clear boundary between rice and other landforms and enhance the contrast, so as to generate a high-quality rice distribution map, especially when the difference between rice and the background or other landforms is not obvious.

[0057] For example, since the second multi-band SAR image synthesized above contains the backscatter coefficient combination values ​​of five bands, the five bands are 1, 2, 3, 4, and 5, respectively, where 1 is the first band of the transplanting period, 2 is the second band of the transplanting period, 3 is the first band of the filling period, 4 is the jointing period band, and 5 is the second band of the filling period. Histograms are formed for the five bands respectively, and the trough values ​​M on both sides of each peak are obtained. The trough values ​​of the backscatter coefficient combination at different phases are recorded as M nΔTn(n is the number of bands, n=1, 2, 3, 4, 5, 1 and 2 are the sum of the backscattering coefficients corresponding to the two transplanting periods (VV+VH), 3 is the ratio of the backscattering coefficients corresponding to the first phase of the filling period (VH / VV), 4 is the backscattering coefficient Dprvivv (4*VH / (VH+VV)) corresponding to the jointing period, 5 is the backscattering coefficient Dprvivv (4*VH / (VH+VV)) corresponding to the second phase of the filling period).

[0058] Among them, ΔT is determined as follows:

[0059] Let X be the trough number of the backscatter coefficient combination statistical result curve, and count the backscatter coefficient combination values ​​corresponding to the five bands of the second multi-band SAR image to form a histogram (the horizontal axis is the backscatter coefficient combination value, and the vertical axis is the proportion of the distribution quantity). The proportion of rice distribution corresponding to the second multi-band SAR image, the backscatter coefficient combination value is recorded as A, its proportion is recorded as B, and the total proportion of the backscatter coefficient in a certain section is recorded as C. The specific expression is:

[0060]

[0061] For each band n, select the X value when the backscatter coefficient segment ratio C is closest to the obtained rice planting ratio information.

[0062] Based on the determined X value, the ΔT corresponding to each band is determined. The specific expression is:

[0063]

[0064] Substitute the ΔT corresponding to each band into M nΔTn , and obtain the corresponding classification threshold.

[0065] 204. Perform initial segmentation on the first multi-band SAR image according to a first preset scale to obtain a plurality of first multi-band SAR sub-images.

[0066] In this step, the first preset scale can be specifically determined based on factors such as the spatial resolution of the image and the size of the object. The scale determines the size of each sub-image after the initial segmentation. For example, the first preset scale: 0.4 (shape), 0.5 (density), and the scale parameter is 1. Select a suitable segmentation algorithm according to the characteristics of the SAR image, such as pixel-based fixed window segmentation or region growth-based segmentation method. Use the selected segmentation algorithm to segment the first multi-band SAR image according to the first preset scale to obtain multiple non-overlapping first multi-band SAR sub-images.

[0067] 205. Calculate the combined denoising threshold of backscatter coefficient based on the minimum backscatter coefficient of each type of ground objects in the plurality of first multi-band SAR sub-images.

[0068] The backscatter coefficient combined denoising threshold is used to remove background noise in a plurality of first multi-band SAR sub-images.

[0069] It should be noted that the backscatter coefficient combination denoising threshold is calculated based on the minimum value of the backscatter coefficient combination values ​​of various ground objects in multiple first multi-band SAR sub-images. The specific expression is:

[0070] ΔB <N×T min ;

[0071] Where ΔB is the combined denoising threshold of the backscattering coefficient used to remove background noise, T min It is the minimum value of the combination of backscatter coefficients of various types of ground objects, and N is a natural number greater than or equal to 2.

[0072] In this step, various types of objects include but are not limited to paddy fields, buildings, forests, etc. The choice of N depends on the intensity of the background noise and the degree of detail of the objects that need to be retained. Traverse all sub-images and record the minimum value in each category to find the minimum value T of the backscatter coefficient of each type of object in all sub-images min Substituting it into the above expression, we can get the combined denoising threshold of the backscatter coefficient. In general, ΔB usually needs to be much larger than T min The choice of N depends on the intensity of background noise and the degree of detail of the objects to be retained. Specifically, 8, 10, etc. can be selected to ensure that ΔB can effectively reduce noise without accidentally deleting important object information.

[0073] 206. Remove background noises from a plurality of first multi-band SAR sub-images respectively according to a combined denoising threshold of a backscatter coefficient, and merge the plurality of denoised first multi-band SAR sub-images to obtain a new first multi-band SAR image of the current year.

[0074] In this step, the calculated backscatter coefficient combined denoising threshold ΔB is applied to each first multi-band SAR sub-image, and by comparing the backscatter coefficient of each pixel with ΔB, it is decided whether to retain the information of the pixel. For pixels below ΔB, they are regarded as background noise and removed, while for pixels above or equal to ΔB, their information is retained. This process can be achieved by binarization or other filtering techniques. All denoised first multi-band SAR sub-images are reassembled into a complete image to ensure seamless connection between the parts, forming a new first multi-band SAR image of the current year.

[0075] 207. Perform secondary segmentation on the new first multi-band SAR image according to a second preset scale to obtain a plurality of new first multi-band SAR sub-images.

[0076] The second preset scale is greater than the first preset scale.

[0077] In this step, the second preset scale can also be determined based on factors such as the spatial resolution of the image, the size of the object, etc. However, it is usually larger than the first preset scale, because a larger segmentation scale can help capture the changes or patterns of objects in a larger range, which is suitable for different levels of analysis needs. The same segmentation algorithm as the initial segmentation is applied to perform a secondary segmentation on the new first multi-band SAR image according to your second preset scale to generate multiple new first multi-band SAR sub-images. In order to execute step 208.

[0078] 208. Introduce the backscatter coefficient combination threshold corresponding to each band into the preset extraction rule, and use the extraction rule to extract rice from multiple new first multi-band SAR sub-images to obtain the rice distribution map corresponding to the extracted area in the current year.

[0079] In this step, combined with the description in step 207 of the above method, it is only necessary to replace the first multi-band SAR image with a plurality of new first multi-band SAR sub-images, so the same contents will not be repeated here.

[0080] It should be noted that the combined classification threshold of the backscatter coefficient corresponding to each band is introduced into the preset extraction rule, and the extraction rule is used to extract rice from the first multi-band SAR image of the current year to obtain the rice distribution map corresponding to the area to be extracted in the current year. The specific execution process is as follows: the combined classification threshold of the backscatter coefficient corresponding to the transplanting period band and the jointing period band is used as the extraction upper limit, and the combined classification threshold of the backscatter coefficient corresponding to the filling period band is used as the extraction lower limit to obtain the rice extraction results corresponding to each band; based on the rice extraction results corresponding to each band, the rice distribution map corresponding to the area to be extracted in the current year is generated.

[0081] Among them, the various bands are the transplanting period band, the jointing period band and the filling period band.

[0082] In this step, the combined classification threshold of the backscatter coefficient corresponding to the transplanting period band and the jointing period band is used as the upper limit of extraction, that is, when the backscatter coefficient of the pixel is lower than these two thresholds, it is not classified as rice. The combined classification threshold of the backscatter coefficient corresponding to the filling period band is used as the lower limit of extraction, that is, when the backscatter coefficient of the pixel is higher than this threshold, it is considered to be rice. According to the upper and lower limits set above, a specific extraction rule is formulated. For example, a logical expression can be defined to determine whether each pixel belongs to the rice category. The extraction rule is used to classify each pixel in the first multi-band SAR image of the current year according to its backscatter coefficient in the transplanting period, jointing period and filling period bands. The classification results of all pixels are summarized to form a complete rice distribution map. In this process, spatial filtering or morphological operations (such as expansion and corrosion) can be applied to improve the spatial coherence of the classification results and reduce the influence of isolated noise points. Smoothing algorithms (such as Gaussian filtering) can also be used to process the boundaries of the classification results to make the rice distribution map smoother and more natural.

[0083] Continuing with the example in step 203, illustratively, in combination with the above description, since the transplanting period band includes the first-phase band of the transplanting period and the second-phase band of the transplanting period, and the filling period band includes the first-phase band of the filling period and the second-phase band of the filling period, the rice extraction is performed based on the multiple first multi-band SAR sub-images after secondary segmentation, and the specific logical expression is as follows:

[0084] S t1 ≤M 1ΔT1 ; ①

[0085] S t2 ≤M 2ΔT2 ; ②

[0086] R t4 ≥M 3ΔT3 ; ③

[0087] D t3 ≤M 4ΔT4 ; ④

[0088] D t4 ≥M 5ΔT5 ⑤

[0089] ①∪②∩③∩④∩⑤

[0090] Among them, S is the sum of the backscattering coefficients of the corresponding phase (VV+VH), R is the ratio of the backscattering coefficients of the corresponding phase (VH / VV), D is the backscattering coefficient Dprvivv (4*VH / (VH+VV)) of the corresponding phase, t1 and t2 are the phases corresponding to the two transplanting periods, t3 is the phase corresponding to the jointing stage, and t4 and t5 are the phases corresponding to the two filling stages.

[0091] Furthermore, in order to further remove the interference noise in the rice distribution map and ensure the accuracy of the rice distribution map. Specifically, after introducing the combined classification threshold of the backscatter coefficient corresponding to each band into the preset extraction rule, and using the extraction rule to extract rice from the first multi-band SAR image of the current year, and obtaining the rice distribution map corresponding to the area to be extracted in the current year, the method also includes: calculating the number of pixels occupied by each rice patch in the rice distribution map; determining the pixel number threshold for removing interference noise according to the resolution corresponding to the first multi-phase SAR data and the preset result accuracy requirement, and the pixel number threshold is used to remove interference noise in the rice patch; defining the rice patch with a pixel number less than or equal to the pixel number threshold as a noise patch, and marking the pixels occupied by the noise patch as non-rice pixels, to obtain the final rice distribution map.

[0092] In this step, connected component analysis or region growing algorithm can be used to identify all independent rice patches in the rice distribution map. For each patch, the number of pixels contained in it is counted and this information is recorded. Specifically, a table or database can be created to store the unique identifier, location information (such as center coordinates), number of pixels and other attributes of each patch. The resolution corresponding to the first multi-phase SAR data is analyzed to understand the actual ground area represented by each pixel, and the maximum acceptable error range is defined according to the preset result accuracy requirements to ensure that the final result can meet the application requirements. According to the above analysis results, a reasonable pixel number threshold is set as a standard for distinguishing between real rice patches and noise patches. Compare the number of pixels of each patch with the set pixel number threshold, and define the patches with a pixel number less than or equal to the pixel number threshold as noise patches. The pixels occupied by these noise patches can be re-marked as non-rice pixels on the original rice distribution map. At the same time, modify the corresponding records in the patch attribute table to remove the patch information marked as noise. According to the updated patch attribute table, the rice distribution map is reconstructed to ensure that only real rice patches with a pixel number greater than the threshold are retained to obtain the final rice distribution map.

[0093] It should be noted that the pixel number threshold for removing interference noise is determined according to the resolution corresponding to the first multi-temporal SAR data and the preset result accuracy requirement. The specific expression is:

[0094]

[0095] Among them, P is the pixel number threshold for removing interference noise, Q is the result accuracy requirement, specifically referring to the minimum ground area that each rice patch should cover in the expected result (for example, in square meters), and E is the resolution corresponding to the first multi-temporal SAR data, specifically referring to the actual ground length represented by each pixel (for example, in meters / pixel).

[0096] Through the specific expression given in this step, the interference noise can be further effectively removed from the rice distribution map, the quality of the final classification result can be improved, and the accuracy of rice distribution can be ensured.

[0097] Furthermore, as a response to the above Figure 1-2 The implementation of the method embodiment shown in the figure, the embodiment of the present application provides a rice extraction device based on multi-phase SAR data, which is used to improve the accuracy of rice identification and extraction. The embodiment of the device corresponds to the aforementioned method embodiment. For ease of reading, this embodiment will no longer repeat the details of the aforementioned method embodiment one by one, but it should be clear that the device in this embodiment can correspond to all the contents of the aforementioned method embodiment. Specifically, Figure 3 As shown, the device comprises:

[0098] An acquisition unit 301 is used to acquire multi-phase SAR data corresponding to a target year of a region to be extracted and a backscatter coefficient corresponding to the multi-phase SAR data, wherein the target year includes a current year and a historical year;

[0099] The processing unit 302 is used to synthesize the multi-phase SAR data obtained by the acquisition unit 301 into a multi-band SAR image according to the backscattering coefficient combination mode preset for different phases, wherein the multi-band SAR image includes a first multi-band SAR image of the current year and a second multi-band SAR image of the historical year;

[0100] The first determining unit 303 is used to determine the backscatter coefficient combination classification threshold corresponding to each band according to the backscatter coefficient combination value corresponding to each band in the second multi-band SAR image of the historical year obtained by the processing unit 302 and the proportion of rice distribution;

[0101] The extraction unit 34 is used to introduce the backscatter coefficient combination classification threshold corresponding to each band obtained by the first determination unit 303 into a preset extraction rule, and use the extraction rule to extract rice from the first multi-band SAR image of the current year obtained by the processing unit to obtain a rice distribution map corresponding to the area to be extracted in the current year.

[0102] Further, such as Figure 4 As shown, the acquisition unit 301 includes:

[0103] The first determination module 3011 is used to determine the multi-phase corresponding to rice according to the phenological characteristics of the landforms in the area to be extracted, wherein the multi-phase includes the transplanting period, the jointing period and the filling period;

[0104] An acquisition module 3012 is configured to acquire SAR data corresponding to the target year of the area to be extracted according to the multi-phase obtained by the first determination module 3011, and obtain the multi-phase SAR data, wherein the multi-phase SAR data includes first multi-phase SAR data of the current year and second multi-phase SAR data of the historical year;

[0105] The preprocessing module 3013 is used to preprocess the first multi-phase SAR data and the second multi-phase SAR data obtained by the acquisition module 3012 respectively to obtain the backscattering coefficients corresponding to the first multi-phase SAR data and the second multi-phase SAR data respectively.

[0106] Further, such as Figure 4 As shown, the first determining unit 303 includes:

[0107] A drawing module 3031 is used to draw a histogram corresponding to each band based on the backscatter coefficient combination values ​​corresponding to each band in the second multi-band SAR image and the proportion of the distribution quantity corresponding to the backscatter coefficient combination values, wherein the abscissa of the histogram is the backscatter coefficient combination value and the ordinate is the proportion of the distribution quantity;

[0108] The second determination module 3032 is used to refer to the proportion of rice distribution corresponding to each band, and use the proximity principle to determine the classification troughs corresponding to each band in the troughs on both sides of each peak in the histogram corresponding to each band obtained by the drawing module 3031, and use the backscatter coefficient combination value corresponding to the classification trough as the backscatter coefficient combination threshold corresponding to each band.

[0109] Further, such as Figure 4 As shown, the device also includes:

[0110] A first segmentation unit 305 is used to perform initial segmentation on the first multi-band SAR image according to a first preset scale before the extraction unit 304 to obtain a plurality of first multi-band SAR sub-images;

[0111] A first calculation unit 306 is used to calculate a backscatter coefficient combination denoising threshold based on the minimum backscatter coefficients of various types of objects in the multiple first multi-band SAR sub-images obtained by the first segmentation unit 305, wherein the backscatter coefficient combination denoising threshold is used to remove background noise in the multiple first multi-band SAR sub-images;

[0112] A first denoising unit 307 is configured to remove background noises in a plurality of first multi-band SAR sub-images according to the backscattering coefficient combined denoising threshold obtained by the first calculating unit 306, and merge the plurality of denoised first multi-band SAR sub-images to obtain a new first multi-band SAR image of the current year;

[0113] A second segmentation unit 308 is used to perform secondary segmentation on the new first multi-band SAR image obtained by the first denoising unit 307 according to a second preset scale to obtain a plurality of new first multi-band SAR sub-images, wherein the second preset scale is larger than the first preset scale;

[0114] The extraction unit 304 is specifically used to:

[0115] The backscatter coefficient combination threshold corresponding to each band is introduced into a preset extraction rule, and the extraction rule is used to extract rice from the multiple new first multi-band SAR sub-images obtained by the second segmentation unit 308 to obtain a rice distribution map corresponding to the area to be extracted in the current year.

[0116] Further, such as Figure 4 As shown, the first calculation unit 306 is specifically expressed as:

[0117] ΔB <N×T min ;

[0118] Where ΔB is the combined denoising threshold of the backscattering coefficient used to remove background noise, T min It is the minimum value of the combination of backscatter coefficients of various types of ground objects, and N is a natural number greater than or equal to 2.

[0119] Further, such as Figure 4 As shown, the bands are respectively a transplanting period band, a jointing period band and a filling period band; the extraction unit 304 includes:

[0120] The extraction module 3041 is used to use the backscatter coefficient combination classification threshold corresponding to the transplanting period band and the jointing period band as the extraction upper limit, and use the backscatter coefficient combination classification threshold corresponding to the filling period band as the extraction lower limit, to obtain the rice extraction results corresponding to each band;

[0121] The generating module 3042 is used to generate a rice distribution map corresponding to the area to be extracted in the current year based on the rice extraction results corresponding to each band obtained by the extracting module 3041.

[0122] Further, such as Figure 4 As shown, the device also includes:

[0123] A second calculation unit 309 is used to calculate the number of pixels occupied by each rice patch in the rice distribution map after the extraction unit 304;

[0124] A second determining unit 310 is used to determine a pixel number threshold for removing interference noise according to a resolution corresponding to the first multi-temporal SAR data and a preset result accuracy requirement, wherein the pixel number threshold is used for removing interference noise in the rice patch;

[0125] The second denoising unit 311 is used to define the rice patch whose number of pixels obtained by the second calculating unit 309 is less than or equal to the pixel number threshold obtained by the second determining unit 310 as a noise patch, and mark the pixels occupied by the noise patch as non-rice pixels, so as to obtain a final rice distribution map.

[0126] Further, such as Figure 4 As shown, the second determining unit 310 is specifically expressed as:

[0127]

[0128] Among them, P is the pixel number threshold for removing interference noise, Q is the result accuracy requirement, and E is the resolution corresponding to the first multi-temporal SAR data.

[0129] Furthermore, an embodiment of the present application further provides a storage medium, wherein the storage medium is used to store a computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the above Figure 1-2 The rice extraction method based on multi-temporal SAR data described in.

[0130] Furthermore, the embodiment of the present application also provides a processor, the processor is used to run a program, wherein the program executes the above Figure 1-2 The rice extraction method based on multi-temporal SAR data described in.

[0131] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0132] It is understandable that the related features in the above methods and devices can be referenced to each other. In addition, the "first", "second" and the like in the above embodiments are used to distinguish the embodiments, but do not represent the advantages and disadvantages of the embodiments.

[0133] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0134] The algorithm and display provided herein are not inherently related to any particular computer, virtual system or other device. Various general purpose systems can also be used together with the teachings based on this. According to the above description, it is obvious that the structure required for constructing such systems. In addition, the application is not directed to any specific programming language either. It should be understood that various programming languages ​​can be utilized to realize the content of the application described herein, and the description of the specific language above is for the purpose of disclosing the best mode of implementation of the application.

[0135] In addition, the memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0136] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0137] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0138] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0139] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0140] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0141] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0142] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0143] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0144] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0145] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A rice extraction method based on multi-temporal SAR data, characterized in that: The method comprises: Acquire multi-phase SAR data corresponding to the target year of the area to be extracted and backscatter coefficients corresponding to the multi-phase SAR data, wherein the target year includes the current year and historical years; The multi-phase SAR data are synthesized into a multi-band SAR image according to a combination method of backscatter coefficients preset for different phases, wherein the multi-band SAR image includes a first multi-band SAR image of the current year and a second multi-band SAR image of the historical year; Determine the backscatter coefficient combination classification threshold corresponding to each band according to the backscatter coefficient combination value corresponding to each band in the second multi-band SAR image of the historical year and the proportion of rice distribution; The backscatter coefficient combination classification threshold corresponding to each band is introduced into a preset extraction rule, and the extraction rule is used to extract rice from the first multi-band SAR image of the current year to obtain a rice distribution map corresponding to the area to be extracted in the current year.

2. The method according to claim 1, characterized in that Acquiring multi-temporal SAR data corresponding to the target year and backscatter coefficients corresponding to the multi-temporal SAR data of the area to be extracted, including: Determine the multi-temporal phases corresponding to rice according to the phenological characteristics of the landforms in the area to be extracted; Acquire SAR data of the area to be extracted corresponding to the target year according to the multi-phase, and obtain the multi-phase SAR data, wherein the multi-phase SAR data includes first multi-phase SAR data of the current year and second multi-phase SAR data of the historical year; Preprocessing is performed on the first multi-temporal SAR data and the second multi-temporal SAR data respectively to obtain backscatter coefficients corresponding to the first multi-temporal SAR data and the second multi-temporal SAR data.

3. The method according to claim 1, characterized in that According to the backscatter coefficient combination value corresponding to each band in the second multi-band SAR image of the historical year and the proportion of rice distribution, the backscatter coefficient combination threshold corresponding to each band is determined, including: Based on the backscatter coefficient combination values ​​corresponding to each band in the second multi-band SAR image and the proportion of the distribution quantity corresponding to the backscatter coefficient combination values, a histogram corresponding to each band is drawn, wherein the abscissa of the histogram is the backscatter coefficient combination value, and the ordinate is the proportion of the distribution quantity; Referring to the proportion of rice distribution corresponding to each band, the classification trough corresponding to each band is determined in the troughs on both sides of each peak in the histogram corresponding to each band using the proximity principle, and the backscatter coefficient combination value corresponding to the classification trough is used as the backscatter coefficient combination threshold corresponding to each band.

4. The method according to claim 1, characterized in that: Before introducing the backscatter coefficient combination classification threshold corresponding to each band into a preset extraction rule, and using the extraction rule to extract rice from the first multi-band SAR image of the current year to obtain a rice distribution map of the area to be extracted corresponding to the current year, the method further includes: Performing an initial segmentation on the first multi-band SAR image according to a first preset scale to obtain a plurality of first multi-band SAR sub-images; Calculating a backscatter coefficient combination denoising threshold based on minimum backscatter coefficients of various types of ground objects in a plurality of the first multi-band SAR sub-images, wherein the backscatter coefficient combination denoising threshold is used to remove background noise in the plurality of the first multi-band SAR sub-images; Removing background noise from a plurality of first multi-band SAR sub-images respectively according to the backscatter coefficient combined denoising threshold, and merging the plurality of denoised first multi-band SAR sub-images to obtain a new first multi-band SAR image of the current year; Performing secondary segmentation on the new first multi-band SAR image according to a second preset scale to obtain a plurality of new first multi-band SAR sub-images, wherein the second preset scale is larger than the first preset scale; The backscatter coefficient combination threshold corresponding to each band is introduced into a preset extraction rule, and the extraction rule is used to extract rice from the first multi-band SAR image of the current year to obtain a rice distribution map corresponding to the current year of the area to be extracted, including: The backscatter coefficient combination threshold corresponding to each band is introduced into a preset extraction rule, and the extraction rule is used to extract rice from a plurality of the new first multi-band SAR sub-images to obtain a rice distribution map corresponding to the area to be extracted in the current year.

5. The method according to claim 4, characterized in that The backscatter coefficient combination denoising threshold is calculated based on the minimum value of the backscatter coefficient combination values ​​of various ground objects in the first multi-band SAR sub-images. The specific expression is: ΔB <N×T min ; Where ΔB is the combined denoising threshold of the backscattering coefficient used to remove background noise, T min It is the minimum value of the combination of backscatter coefficients of various types of ground objects, and N is a natural number greater than or equal to 2.

6. The method according to any one of claims 1 to 5, characterized in that The bands are respectively a transplanting period band, a jointing period band and a filling period band; the backscatter coefficient combination classification threshold corresponding to each band is introduced into a preset extraction rule, and the extraction rule is used to extract rice from the first multi-band SAR image of the current year, so as to obtain a rice distribution map corresponding to the area to be extracted in the current year, including: The backscatter coefficient combination classification thresholds corresponding to the transplanting period band and the jointing period band are used as the extraction upper limit, and the backscatter coefficient combination classification thresholds corresponding to the filling period band are used as the extraction lower limit, to obtain the rice extraction results corresponding to each band; A rice distribution map corresponding to the area to be extracted in the current year is generated based on the rice extraction results corresponding to each band.

7. The method according to any one of claims 1 to 5, characterized in that After introducing the backscatter coefficient combination classification threshold corresponding to each band into a preset extraction rule, and using the extraction rule to extract rice from the first multi-band SAR image of the current year to obtain a rice distribution map of the area to be extracted corresponding to the current year, the method further includes: Calculating the number of pixels occupied by each rice patch in the rice distribution map; Determining a pixel number threshold for removing interference noise according to a resolution corresponding to the first multi-temporal SAR data and a preset result accuracy requirement, wherein the pixel number threshold is used for removing interference noise in the rice patch; The rice patches whose pixel number is less than or equal to the pixel number threshold are defined as noise patches, and the pixels occupied by the noise patches are marked as non-rice pixels, so as to obtain a final rice distribution map.

8. A rice extraction device based on multi-temporal SAR data, characterized in that: The device comprises: An acquisition unit, used to acquire multi-phase SAR data corresponding to a target year of an area to be extracted and a backscatter coefficient corresponding to the multi-phase SAR data, wherein the target year includes a current year and a historical year; a processing unit, configured to synthesize the multi-phase SAR data obtained by the acquisition unit into a multi-band SAR image according to a combination of backscatter coefficients preset for different phases, wherein the multi-band SAR image includes a first multi-band SAR image of the current year and a second multi-band SAR image of the historical year; A first determining unit is used to determine a backscatter coefficient combination classification threshold corresponding to each band according to the backscatter coefficient combination value corresponding to each band in the second multi-band SAR image of the historical year obtained by the processing unit and the proportion of rice distribution; The extraction unit is used to introduce the backscatter coefficient combination classification threshold corresponding to each band obtained by the first determination unit into a preset extraction rule, and use the extraction rule to extract rice from the first multi-band SAR image of the current year obtained by the processing unit to obtain a rice distribution map corresponding to the area to be extracted in the current year.

9. A storage medium, characterized in that: The storage medium includes a stored program, wherein when the program is executed, the device where the storage medium is located is controlled to execute the rice extraction method based on multi-temporal SAR data according to any one of claims 1 to 7.

10. A processor, characterized in that: The processor is used to run a program, wherein the program, when running, executes the rice extraction method based on multi-temporal SAR data according to any one of claims 1 to 7.

Images